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<div class="moz-cite-prefix">Done. Couldn't raise a feature request
via the web interface. So, dropped a mail.<br>
<br>
Thanks,<br>
Rahul.<br>
<br>
On Sunday 28 December 2014 11:44 AM, <a class="moz-txt-link-abbreviated" href="mailto:anders.haal@ingby.com">anders.haal@ingby.com</a> wrote:<br>
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<blockquote cite="mid:549F9FAD.103@ingby.com" type="cite">
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I have looked into the topic a little bit more and I think the
capability to detect outliers is an important functionality
pointed out by Rahul.<br>
I think we should try to get some functionality like the MAD
approach into the next version. <br>
@Rahul - please make a feature request on this topic. <br>
Anders <br>
<br>
<br>
<br>
<div class="moz-cite-prefix">On 12/17/2014 09:57 PM, Anders Håål
wrote:<br>
</div>
<blockquote cite="mid:5491EE3F.90602@ingby.com" type="cite">
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<div class="moz-cite-prefix">Sorry for the link - <a
moz-do-not-send="true" class="moz-txt-link-freetext"
href="http://stats.stackexchange.com/questions/38001/detecting-outliers-using-standard-deviations">http://stats.stackexchange.com/questions/38001/detecting-outliers-using-standard-deviations</a><br>
<br>
<br>
The problem is not to write the code, the problem is to find a
logic to determine which numbers to remove from the data set.
What is a deviation from the normal difference in the set? <br>
<br>
Googling a bit more I found these definitions that may be
applicable using stdev for your use case:<br>
<br>
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charset=utf-8">
<b>Mean and Standard Deviation Method</b><b><br>
</b>For this outlier detection method, the mean and standard
deviation of the residuals are calculated and compared. If a
value is a certain number of standard deviations away from the
mean, that data point is identified as an outlier. The
specified number of standard deviations is called the
threshold. The default value is 3.<br>
<br>
This method can fail to detect outliers because the outliers
increase the standard deviation. The more extreme the outlier,
the more the standard deviation is affected.<br>
<br>
<b>Median and Median Absolute Deviation Method (MAD)</b><b><br>
</b><br>
For this outlier detection method, the median of the residuals
is calculated. Then, the difference is calculated between each
historical value and this median. These differences are
expressed as their absolute values, and a new median is
calculated and multiplied by an empirically derived constant
to yield the median absolute deviation (MAD). If a value is a
certain number of MAD away from the median of the residuals,
that value is classified as an outlier. The default threshold
is 3 MAD.<br>
<br>
This method is generally more effective than the mean and
standard deviation method for detecting outliers, but it can
be too aggressive in classifying values that are not really
extremely different. Also, if more than 50% of the data points
have the same value, MAD is computed to be 0, so any value
different from the residual median is classified as an
outlier.<br>
<br>
<b>Median and Interquartile Deviation Method (IQD)</b><br>
<br>
For this outlier detection method, the median of the residuals
is calculated, along with the 25th percentile and the 75th
percentile. The difference between the 25th and 75th
percentile is the interquartile deviation (IQD). Then, the
difference is calculated between each historical value and the
residual median. If the historical value is a certain number
of MAD away from the median of the residuals, that value is
classified as an outlier. The default threshold is 2.22, which
is equivalent to 3 standard deviations or MADs.<br>
<br>
This method is somewhat susceptible to influence from extreme
outliers, but less so than the mean and standard deviation
method. Box plots are based on this approach. The median and
interquartile deviation method can be used for both symmetric
and asymmetric data.<br>
<br>
If you find a method that you think could work, we could
implement it together and you can verify it with your data.
Can you say anything about the data collected?<br>
Anders <br>
<br>
On 12/17/2014 09:25 PM, Rahul Amaram wrote:<br>
</div>
<blockquote cite="mid:5491E6AE.5000901@vizury.com" type="cite">Hi
Andre, <br>
<br>
So, I would like to remove the outlier and calculate the mean
for the remaining elements. Any suggestion apart from writing
my own custom math function? Also, I don't think that you have
shared the link. <br>
<br>
Thanks, <br>
Rahul. <br>
<br>
On Thursday 18 December 2014 12:55 AM, Anders Håål wrote: <br>
<blockquote type="cite">Hi Rahul, <br>
Its possible, but the question is what algorithm to use. The
second question would also be what would you do with the
remaining set, calculate a mean? <br>
When it comes to exclude a deviant value it sound close to
what is called a outlier, <a moz-do-not-send="true"
class="moz-txt-link-freetext"
href="http://en.wikipedia.org/wiki/Outlier">http://en.wikipedia.org/wiki/Outlier</a>.
There are a number of mathematical solutions to this
problem, but not sure which would be applicable or correct.
Check this link for a discussions on the topic where one
approach is using standard deviation, but from the
discussion it does not sound like a statistical correct
approach. <br>
<br>
If you or anyone else on this list find an good approach, I
more then happy to try it. In Bischeck its possible to plug
in your own functions as described in <a
moz-do-not-send="true" class="moz-txt-link-freetext"
href="http://www.bischeck.org/wp-content/uploads/2014/06/Bischeck_installation_and_administration_guide.html#toc-Section-6.2">http://www.bischeck.org/wp-content/uploads/2014/06/Bischeck_installation_and_administration_guide.html#toc-Section-6.2</a>
so you can easily do your own testing. Using the cache
browser cli <a moz-do-not-send="true"
class="moz-txt-link-freetext"
href="http://www.bischeck.org/wp-content/uploads/2014/06/Bischeck_installation_and_administration_guide.html#toc-Section-4.4">http://www.bischeck.org/wp-content/uploads/2014/06/Bischeck_installation_and_administration_guide.html#toc-Section-4.4</a>
you can easily test your function. <br>
<br>
Anders <br>
<br>
<br>
On 12/17/2014 03:40 PM, Rahul Amaram wrote: <br>
<blockquote type="cite">Hi, <br>
<br>
I had a quick question. Let us say we calculate the
threshold based on the values of the past six days, one
value per day. Now let us say, out of 6 values, one of
these values is way too deviant. Then is it possible to
exclude this deviant value from calculating the threshold?
<br>
<br>
Thanks, <br>
Rahul. <br>
</blockquote>
<br>
<br>
</blockquote>
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Ingby <a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="http://www.ingby.com"><http://www.ingby.com></a>
IngbyForge <a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="http://gforge.ingby.com"><http://gforge.ingby.com></a>
bischeck - dynamic and adaptive monitoring for Nagios <a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="http://www.bischeck.org"><http://www.bischeck.org></a>
<a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="mailto:anders.haal@ingby.com">anders.haal@ingby.com</a><a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="mailto:anders.haal@ingby.com"><mailto:anders.haal@ingby.com></a>
Mjukvara genom ingenjörsmässig kreativitet och kompetens
Ingenjörsbyn
Box 531
101 30 Stockholm
Sweden
<a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="http://www.ingby.com">www.ingby.com</a> <a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="http://www.ingby.com/"><http://www.ingby.com/></a>
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